Evidence map›Paper›PMID 33373409›Full record

ArticlePloS one2020

Estimating the impact of differential adherence on the comparative effectiveness of stool-based colorectal cancer screening using the CRC-AIM microsimulation model.

Andrew Piscitello, Leila Saoud, A Mark Fendrick, Bijan J Borah, Kristen Hassmiller Lich, Michael Matney, A Burak Ozbay, Marcus Parton, Paul J Limburg

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.7field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
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  13. Circulating cell-free DNA for cancer early detection.Innovation (Cambridge (Mass.)) · 2022
    Review
  14. Optimal Strategies for Colorectal Cancer Screening.Current treatment options in oncology · 2022
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Detection of Postcolonoscopy Colorectal Neoplasia by Multi-target Stool DNA.Clinical and translational gastroenterology · 2021
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Andrew PiscitelloEmpiriQA, LLC, Long Grove, IL, United States of America.ORCID 0000-0001-7471-6472
Leila SaoudExact Sciences Corporation, Madison, WI, United States of America.
A Mark FendrickDivision of Gastroenterology, University of Michigan, Ann Arbor, MI, United States of America.
Bijan J BorahDepartment of Health Services Research, Mayo Clinic, Rochester, MN, United States of America.
Kristen Hassmiller LichDepartment of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
Michael MatneyExact Sciences Corporation, Madison, WI, United States of America.
A Burak OzbayExact Sciences Corporation, Madison, WI, United States of America.
Marcus PartonExact Sciences Corporation, Madison, WI, United States of America.
Paul J LimburgDivision of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States of America.
Exact Sciences (United States) · USMayo Clinic in Arizona · USMayo Clinic in Florida · USUniversity of Michigan · USUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReal-world adherence to colorectal cancer (CRC) screening strategies is imperfect. The CRC-AIM microsimulation model was used to estimate the impact of imperfect adherence on the relative benefits and burdens of guideline-endorsed, stool-based screening strategies.

methodsPredicted outcomes of multi-target stool DNA (mt-sDNA), fecal immunochemical tests (FIT), and high-sensitivity guaiac-based fecal occult blood tests (HSgFOBT) were simulated for 40-year-olds free of diagnosed CRC. For robustness, imperfect adherence was incorporated in multiple ways and with extensive sensitivity analysis. Analysis 1 assumed adherence from 0%-100%, in 10% increments. Analysis 2 longitudinally applied real-world first-round differential adherence rates (base-case imperfect rates = 40% annual FIT vs 34% annual HSgFOBT vs 70% triennial mt-sDNA). Analysis 3 randomly assigned individuals to receive 1, 5, or 9 lifetime (9 = 100% adherence) mt-sDNA tests and 1, 5, or 9 to 26 (26 = 100% adherence) FIT tests. Outcomes are reported per 1000 individuals compared with no screening.

resultsEach screening strategy decreased CRC incidence and mortality versus no screening. In individuals screened between ages 50-75 and adherence ranging from 10%a-100%, the life-years gained (LYG) for triennial mt-sDNA ranged from 133.1-300.0, for annual FIT from 96.3-318.1, and for annual HSgFOBT from 99.8-320.6. At base-case imperfect adherence rates, mt-sDNA resulted in 19.1% more LYG versus FIT, 25.4% more LYG versus HSgFOBT, and generally had preferable efficiency ratios while offering the most LYG. Completion of at least 21 FIT tests is needed to reach approximately the same LYG achieved with 9 mt-sDNA tests.

conclusionsAdherence assumptions affect the conclusions of CRC screening microsimulations that are used to inform CRC screening guidelines. LYG from FIT and HSgFOBT are more sensitive to changes in adherence assumptions than mt-sDNA because they require more tests be completed for equivalent benefit. At imperfect adherence rates, mt-sDNA provides more LYG than FIT or HSgFOBT at an acceptable tradeoff in screening burden.

Indexed as

AgedAged, 80 and overColorectal NeoplasmsDNAEarly Detection of CancerFemaleGuideline AdherenceHumansIncidenceLongitudinal StudiesMaleMiddle AgedModels, TheoreticalMortalityOccult BloodPractice Guidelines as TopicDNA

Identifiers

PMID33373409
PMCPMC7771985
OpenAlexW3116978750

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.